In order to better understand the relationship between feature maps and object positions, we explore the relationship between the object detection models and the FGVC methods. From Faster-RCNN[1]}, YOLO[2]}[3]}, and RetinaNet[4]}, we can find that feature maps can be rich in location and category information. Segmentation methods such as Mask-RCNN[5]} give pixel-level predictions, i.e. fine-grained predictions. Although these methods demonstrate that feature maps are rich in semantics, they are learned by relying on human-annotated region information, unlike the FGVC task, which only uses image-level annotation for training. Therefore, we discuss Weakly Supervised Object Detection(WSOD) methods, such as WSDDN[6]}, OICR[7]}, and WCCN[8]}, etc. The concept of the WSOD method is that the response of feature maps reflects the location of objects, and we can use these feature maps to complete bounding box prediction through an weakly supervised multi-stage architecture and loss function design.
